Some embodiments described herein relate generally to providing an automated system for simultaneous multi-task ensemble learning.
In recent years, vast volumes of empirical data have become available through the widespread use of electronic devices. Artificial intelligence provides methods for discovering logical relations and patterns of behavior within the data and for learning from data. The discovered patterns in data and analysis results provided via various scientific disciplines such as, for example, machine learning provide insights for service providers, managers, manufacturers, business owners and the like to plan proper business strategies and prepare for future trends by predicting events by building prediction models based on the results of data analysis.
The extremely high volume of available data, however, makes the data analysis difficult, expensive, and time consuming. Various techniques are, therefore, adopted to reduce the amount of time and expense spent on data analysis. Data sampling is one such technique, which is concerned with selection of a subset of data from within a statistical population to estimate characteristics of the whole population. In addition to sampling, forecasting and prediction of future events based on the data derived from similar events in the past, includes various processes such as, for example, feature engineering (identifying an appropriate mapping from unstructured to structure data such that the structured data may be used for the purpose of modeling correlated relationships within the data), action labeling (systematic identification of relevant events that become dependent variables in future modeling processes), etc.
In statistics and machine learning, ensemble methods are analysis techniques that use a set of models to obtain better predictive performance for a task than could be obtained from any of the constituent models.
Currently known machine learning techniques, however, require user intervention and lack the capability of automated and simultaneous application of the entire workflow for machine learning methods on data. Therefore, a need exists for systems and methods for automated and simultaneous application of machine learning techniques such as, for example, data sampling, feature engineering, action labeling, etc. on sampled and extracted data for various tasks.